Transforming and evaluating electronic health record disease phenotyping algorithms using the OMOP common data model: a case study in heart failure
2021
Objective: The aim of the study was to transform a resource of linked electronic health records (EHR) to the
OMOP common data model (CDM) and evaluate the process in terms of syntactic and semantic consistency
and quality when implementing disease and risk factor phenotyping algorithms.
Materials and Methods: Using heart failure (HF) as an exemplar, we represented three national EHR sources
(Clinical Practice Research Datalink, Hospital Episode Statistics Admitted Patient Care, Office for National Statistics) into the OMOP CDM 5.2. We compared the original and CDM HF patient population by calculating and presenting descriptive statistics of demographics, related comorbidities, and relevant clinical biomarkers.
Results: We identified a cohort of 502 536 patients with the incident and prevalent HF and converted 1 099 195
384 rows of data from 216 581 914 encounters across three EHR sources to the OMOP CDM. The largest percentage (65%) of unmapped events was related to medication prescriptions in primary care. The average coverage of source vocabularies was >98% with the exception of laboratory tests recorded in primary care. The raw
and transformed data were similar in terms of demographics and comorbidities with the largest difference observed being 3.78% in the prevalence of chronic obstructive pulmonary disease (COPD).
Conclusion: Our study demonstrated that the OMOP CDM can successfully be applied to convert EHR linked
across multiple healthcare settings and represent phenotyping algorithms spanning multiple sources. Similar
to previous research, challenges mapping primary care prescriptions and laboratory measurements still persist
and require further work. The use of OMOP CDM in national UK EHR is a valuable research tool that can enable
large-scale reproducible observational research.
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
30
References
4
Citations
NaN
KQI